



import os
import re
import shutil
import sys
import tempfile
import unittest

import black


a      :  Any			     =			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
a      :  List[str]			     =			'    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'







class 				_a				(   unittest.TestCase					):

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		str							      =     tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir,   """models/bert/"""					)					)
						UpperCAmelCase_:		Dict							      =     self.transformer_dir
						shutil.copy(
						    os.path.join(SCREAMING_SNAKE_CASE_,   """src/transformers/models/bert/modeling_bert.py"""					),   os.path.join(self.transformer_dir,   """models/bert/modeling_bert.py"""					),   )

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     """src/transformers"""
						shutil.rmtree(self.transformer_dir					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=None					)       ->   Any:
						UpperCAmelCase_:		Union[str, Any]							      =     comment + f'\nclass {class_name}(nn.Module):\n' + class_code
						if overwrite_result is not None:
									UpperCAmelCase_:		Dict							      =     comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
						UpperCAmelCase_:		Dict							      =     black.Mode(target_versions={black.TargetVersion.PYaa},   line_length=119					)
						UpperCAmelCase_:		Optional[int]							      =     black.format_str(SCREAMING_SNAKE_CASE_,   mode=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     os.path.join(self.transformer_dir,   """new_code.py"""					)
						with open(SCREAMING_SNAKE_CASE_,   """w""",   newline="""\n"""					) as f:
									f.write(SCREAMING_SNAKE_CASE_					)
						if overwrite_result is None:
									self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE_					)					) == 0					)
						else:
									check_copies.is_copy_consistent(f.name,   overwrite=SCREAMING_SNAKE_CASE_					)
									with open(SCREAMING_SNAKE_CASE_,   """r"""					) as f:
												self.assertTrue(f.read(),   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		str							      =     check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""					)
						self.assertEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						# Base copy consistency
						self.check_copy_consistency(
						    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead""",   """BertLMPredictionHead""",   REFERENCE_CODE + """\n""",   )

						# With no empty line at the end
						self.check_copy_consistency(
						    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead""",   """BertLMPredictionHead""",   SCREAMING_SNAKE_CASE_,   )

						# Copy consistency with rename
						self.check_copy_consistency(
						    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel""",   """TestModelLMPredictionHead""",   re.sub("""Bert""",   """TestModel""",   SCREAMING_SNAKE_CASE_					),   )

						# Copy consistency with a really long name
						UpperCAmelCase_:		Any							      =     """TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
						self.check_copy_consistency(
						    f'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}',   f'{long_class_name}LMPredictionHead',   re.sub("""Bert""",   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					),   )

						# Copy consistency with overwrite
						self.check_copy_consistency(
						    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel""",   """TestModelLMPredictionHead""",   SCREAMING_SNAKE_CASE_,   overwrite_result=re.sub("""Bert""",   """TestModel""",   SCREAMING_SNAKE_CASE_					),   )

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Optional[Any]							      =     check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

						UpperCAmelCase_:		Dict							      =     (
						    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
						    """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
						    """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
						    """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."""
						    """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"""
						    """ released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
						    """ lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"""
						    """ method has been applied to compress GPT2 into"""
						    """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
						    """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
						    """ Multilingual BERT into"""
						    """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
						    """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"""
						    """ (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"""
						    """ as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"""
						    """ Luong, Quoc V. Le, Christopher D. Manning."""
						)
						UpperCAmelCase_:		Optional[Any]							      =     (
						    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
						    """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
						    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
						    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
						)
						UpperCAmelCase_:		int							      =     (
						    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
						    """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
						    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
						    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."""
						    """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"""
						    """ [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
						    """ lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"""
						    """ method has been applied to compress GPT2 into"""
						    """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
						    """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
						    """ Multilingual BERT into"""
						    """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
						    """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"""
						    """ Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"""
						    """ than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"""
						    """ Christopher D. Manning 发布。\n"""
						)

						UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     check_copies.convert_to_localized_md(
						    SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   localized_readme["""format_model_list"""]					)

						self.assertFalse(SCREAMING_SNAKE_CASE_					)
						self.assertEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     check_copies.convert_to_localized_md(
						    SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   localized_readme["""format_model_list"""]					)

						# Check whether the number of models is equal to README.md after conversion.
						self.assertTrue(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Dict							      =     (
						    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
						    """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
						    """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
						    """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."""
						)
						UpperCAmelCase_:		Tuple							      =     (
						    """1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"""
						    """ the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
						    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
						    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
						)
						UpperCAmelCase_:		List[str]							      =     (
						    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
						    """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
						    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
						    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
						)

						UpperCAmelCase_      ,							UpperCAmelCase_:		str							      =     check_copies.convert_to_localized_md(
						    SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   localized_readme["""format_model_list"""]					)

						# Check if the model link is synchronized.
						self.assertEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)











def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			if num <= 0:
						raise ValueError("""Input must be a positive integer"""  )

			UpperCAmelCase_:		Tuple							      =     [True] * (num + 1)

			UpperCAmelCase_:		Tuple							      =     2
			while p * p <= num:
						if primes[p]:
									for i in range(p * p		,						num + 1		,						lowerCAmelCase__  ):
												UpperCAmelCase_:		Optional[Any]							      =     False
						p += 1

			return [prime for prime in range(2		,						num + 1  ) if primes[prime]]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	a      :  Optional[Any]			     =			int(input('Enter a positive integer: ').strip())
	print(prime_sieve_eratosthenes(user_num))






